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  3. Sleep And Activity Patterns In Depression From Wearable Data: Unsupervised Clustering Study.
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  3. Sleep And Activity Patterns In Depression From Wearable Data: Unsupervised Clustering Study.

Related Experiment Video

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder
04:33

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Published on: April 26, 2024

Sleep and Activity Patterns in Depression From Wearable Data: Unsupervised Clustering Study.

Carolin Oetzmann1, Yuezhou Zhang2, Nicholas Cummins2

  • 1Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom, +44 20 7848 0002.

Journal of Medical Internet Research
|June 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Wearable sensors reveal distinct sleep and activity patterns in depression, offering objective subtypes beyond self-reports. These digital phenotypes provide a stable, data-driven approach to understanding major depressive disorder heterogeneity.

Keywords:
Gaussian mixture modelsbehavioral subtypesdigital phenotypinghidden Markov modelslongitudinal datamajor depressive disorderpersonalized psychiatryphysical activitysleep patternsunsupervised learningwearable devices

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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Related Experiment Videos

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Area of Science:

  • Digital phenotyping in mental health research.
  • Application of machine learning for psychiatric disorder subtyping.
  • Objective measurement of behavioral patterns in major depressive disorder.

Background:

  • Depression diagnosis is challenged by symptom heterogeneity and reliance on subjective self-reports.
  • Digital phenotyping offers objective, real-time behavioral and physiological data.
  • Previous subtype discovery was limited by predefined clinical categories and supervised models.

Purpose of the Study:

  • To identify depression subtypes using objective sleep and activity data via unsupervised learning.
  • To analyze temporal transitions between identified behavioral subtypes in major depressive disorder.

Main Methods:

  • Analysis of longitudinal Fitbit data from 623 participants with recurrent major depressive disorder.
  • Application of Gaussian mixture models and hidden Markov models for subtype identification.
  • Robust model selection using grouped cross-validation and seed selection.

    • Main Results:

    • Consistent identification of three activity subtypes (high, light, low) and four sleep subtypes (efficient early/late, disrupted, variable late).
    • Identified subtypes correlate with known depression-behavior associations.
    • Transition modeling indicated stable behavioral phenotypes over time, not just momentary fluctuations.

    Conclusions:

    • Wearable sensor data can identify reproducible, clinically relevant sleep and activity subtypes in major depressive disorder.
    • These objective subtypes may reduce phenotypic heterogeneity and aid research stratification and personalized monitoring.
    • Further validation in independent cohorts is needed to assess predictive utility for depression outcomes.